We hold out 10% of the training data for validation. Hyper-parameter tuning is performed using this validation set through RandomSearch [1]}. To optimize the parameters, Adam optimizer [2]} is used, starting with an initial learning rate of \(1e^{-4}\) . The learnable parameters in the network consists of \(\theta = \lbrace  U_d, D, W_{[1,2,o,s]}, F_{[1,2,3]}, \vec{\textbf {b}}_{[1,2,o,d]}, b_{[1,2,3]} \rbrace \) . Training termination is decided using early stopping technique with a patience of 12. For the batched-modeling of comments in CNNs, each comment is either restricted or padded to 100 words for uniformity. The optimal hyper-parameters are found to be \(\lbrace d_s, d_p, d_t, K\rbrace  = 100\) , \(d_{em} = 300\) , \(k_s = 2\) , \(M = 128\) , and \(\alpha = ReLU\)  (Implementation details are provided in the supplementary).
